We propose a novel neural algorithm for the fundamental problem of computing the entropic
optimal transport (EOT) plan between probability distributions which are accessible by …

Despite the recent advances in the field of computational Schrodinger Bridges (SB), most
existing SB solvers are still heavy-weighted and require complex optimization of several …

Optimal transport (OT) barycenters are a mathematically grounded way of averaging
probability distributions while capturing their geometric properties. In short, the barycenter …

While the field of continuous Entropic Optimal Transport (EOT) has been actively developing
in recent years, it became evident that the classic EOT problem is prone to different issues …

We present a novel approach for offline reinforcement learning that bridges the gap between
recent advances in neural optimal transport and reinforcement learning algorithms. Our key …